Chapter 8: Immune reactions to chronic viruses Theoretical - - PowerPoint PPT Presentation

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Chapter 8: Immune reactions to chronic viruses Theoretical - - PowerPoint PPT Presentation

Chapter 8: Immune reactions to chronic viruses Theoretical Biology 2016 CD4 and CD8 T cells CD8 CD4 HIV life cycle Konstantinov Science 2011 CD4 + T cell From Campbell T cell help Immunity to HIV Cell-intrinsic antiviral immunity?


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SLIDE 1

Theoretical Biology 2016

Chapter 8: Immune reactions to chronic viruses

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SLIDE 2

CD4 and CD8 T cells CD4 CD8

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SLIDE 3

HIV life cycle

From Campbell

Konstantinov Science 2011

CD4+ T cell

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SLIDE 4

NK cell B cell CD8+ CTL CD4+ T cell TH17 cell γδ T cell HIV-1-infected CD4+ T cell DC HIV-1 ? MHC class II MHC class I LILR TCR Peptide ? ? BCR KIR Integration of viral DNA into the host genome Cell-intrinsic antiviral immunity Cell-intrinsic antiviral immunity? Neutralization? Macrophage Cytolysis ER ADCC? ADCVI? ADCP? Cytolysis T cell help T cell help Cell-intrinsic antiviral immunity

  • Walker & Yu, Nat Rev Imm 2013

Immunity to HIV

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SLIDE 5

Time course of an HIV infection

O’Brien & Hendrickson, Genome Biol. 2013

Slow decline of CD4+ T cells: AIDS due to loss of immunity Fairly stable viral setpoint for many years: time to AIDS

CD4 loss: 50-100 cells/year

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SLIDE 6

Viral load predicts rate of disease progression

From: Mellors et al. Science 1996 low V load high V load

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SLIDE 7

Immune response does not correlate with viral load

From: Novitsky et al. J

  • Virol. 2003

Log viral load

Immune response to one protein

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SLIDE 8

Caricature scheme

T I E V

p δV δT δI δE k α β σ

CD4+ T cell Infected immune Effector

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SLIDE 9

Mathematical model

Set δI > δT to allow for cytopathic effects of the virus

dT dt = σ − δTT − βTV , dI dt = βTV − δII − kEI , dV dt = pI − δV V , dE dt = αEI − δEE .

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SLIDE 10

Steady state

¯ I = δE α ¯ V = p δV ¯ I = pδE αδV ¯ T = σ δT + β ¯ V = ασδV αδTδV + pβδE ¯ E = pβ kδV ¯ T − δI k = pβασ k(αδTδV + pβδE) − δI k

Only the rate at which immune cells are activated, α, determines the viral burden I.

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SLIDE 11

Viral load (V), targets (T) and immune response (E) as a function of activation parameter (α)

When α>0.01 the immune response hardly changes, but the viral load (V or I) changes markedly.

Patients having similar immune response can have very different viral loads!

What happens at α=10-4?

(a)

α ¯ V =

p δV ¯

I

10−5 10−4 10−3 10−2 0.1

1 1 10 104 105

(b)

α ¯ T

10−5 10−4 10−3 10−2 0.1

1 1 100 104

(c)

α ¯ E

10−5 10−4 10−3 10−2 0.1

1 0.5 1

¯ V = pδE αδV

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SLIDE 12

Viral load (V), targets (T) and immune response (E) as a function of activation parameter (α)

When α>0.01 the immune response hardly changes, but the viral load (V or I) changes markedly.

Patients having similar immune response can have very different viral loads!

Bifurcation at α=10-4: Immune response disappears

(a)

α ¯ V =

p δV ¯

I

10−5 10−4 10−3 10−2 0.1

1 1 10 104 105

(b)

α ¯ T

10−5 10−4 10−3 10−2 0.1

1 1 100 104

(c)

α ¯ E

10−5 10−4 10−3 10−2 0.1

1 0.5 1

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SLIDE 13

Immune response does not correlate with viral load

From: Novitsky et al. J

  • Virol. 2003

Log viral load

Immune response to one protein

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SLIDE 14

20 40 60 80 100

Time in days

10

3

10

4

10

5

Viral load in blood

δ

Perturb the steady state by treatment (ART)

Treatment:

What can this downslope δ tell us?

Ho and Perelson, Nature 1995, Science 1998

δ

dT dt = σ − δTT − βTV , dI dt = βTV − δII − kEI , dV dt = pI − δV V , dE dt = αEI − δEE .

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SLIDE 15

Separation of time scales: QSSA

dT dt = σ δTT β⇤TI , dI dt = β⇤TI δII kEI , dE dt = αEI δEE ,

Setting dV/dt=0 we obtain V=(p/δV)I, i.e., V becomes proportional to I:

where β’=pβ/δV

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SLIDE 16

Separation of time scales: E=constant

dT dt = σ δTT β⇤TI , dI dt = β⇤TI δII kEI , dE dt = αEI δEE ,

Setting δ = δI + kE we obtain from

dT dt = σ δTT β⇤TI , dI dt = β⇤TI δI ,

which we have seen before and has one steady state:

¯ T = δ β⇤ and ¯ I = σ δ δT β⇤

20 40 60 80 100

Time in days

10

3

10

4

10

5

Viral load in blood

Immune effectors E

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SLIDE 17

Use this model to infer viral dynamics from data

Nature 1995

This paper changed the field: HIV-1 is not slow at all. Utterly simple model teaches us a new biology.

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SLIDE 18

dT dt = σ δTT β⇤TI , dI dt = β⇤TI δI ,

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SLIDE 19

Use model to infer viral dynamics from data

dI dt = β⇤TI δI ,

Famous papers: HIV is not slow but has a generation time of 1-2 days

I(t) = I(0)e−δt

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SLIDE 20

Employing the fitness R0

R0 = β⇤σ δTδ

¯ T = σ δTR0 = K R0 and ¯ I = σ δ

  • 1 1

R0

In this model and we can rewrite the steady state as: where K is the carrying capacity of the target cells. If R0>>1 the steady state of the infected cells should remain approximately σ/δ